2020-06-07T07:45:47Zhttps://zenodo.org/oai2doai:zenodo.org:8087422020-01-25T07:24:06Zsoftwareuser-biogeographyuser-climatechangeuser-ecophysiologyuser-oceanbiogeochemistryMislan, K. A. S.Deutsch, Curtis A.Brill, Richard W.Dunne, John P.Sarmiento, Jorge L.2017-06-14The code in this release reproduces the analysis in the following journal publication:
Mislan, K. A. S., C. A. Deutsch, R. W. Brill, J. P. Dunne, and J. L. Sarmiento. (2017) Projections of climate driven changes in tuna vertical habitat based on species-specific differences in blood oxygen affinity. Global Change Biology.
The CMIP5 P50 Tuna Analysis calculates metrics related to blood-oxygen binding, which is a mechanism determining hypoxia tolerance in the ocean. Blood-oxygen binding is measured as the oxygen pressure in the blood at which whole blood is 50% oxygenated, called P50. A low P50 means that respiratory pigments in the blood of an organism equilibrate to 100% oxygenation at lower oxygen pressures, and the organism is more hypoxia tolerant. Temperature alters hypoxia tolerance by shifting the P50 of organisms. According to results from the Coupled Model Intercomparison Project Phase 5 (CMIP5) RCP 8.5, temperature and oxygen are projected to change by 2100. This analysis calculates the effects of these changes on the blood-oxygen binding and hypoxia tolerance of tuna species. Comparisons are made among tuna species with different physiological adaptations.We thank Hartmut Frenzel for regridding the CMIP5 results. K.A.S. was supported by the Washington Research Foundation Fund for Innovation in Data-Intensive Discovery and the Moore/Sloan Data Science Environments Project at the University of Washington. C.A.D. was supported by grant OCE-1458967 from the National Science Foundation.https://zenodo.org/record/80874210.5281/zenodo.808742oai:zenodo.org:808742url:https://github.com/kallisons/CMIP5_p50_tuna/tree/v1.0doi:10.5281/zenodo.808741url:https://zenodo.org/communities/biogeographyurl:https://zenodo.org/communities/climatechangeurl:https://zenodo.org/communities/ecophysiologyurl:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://www.opensource.org/licenses/MITtunaCMIP5climate changeoxygentemperaturephysiologybloodCMIP5 P50 Tuna Analysis v1.0info:eu-repo/semantics/othersoftwareoai:zenodo.org:161452020-01-25T19:23:10Zsoftwareuser-carboncycleuser-climatechangeuser-ecologicalmodelsuser-oceanbiogeochemistryK. A. S. MislanCharles StockJohn DunneJorge Sarmiento2014-12-31The Microbial Remineralization Model v1.0 simulates the interactions between sinking particles and heterotrophic bacteria in the ocean water column in a 1-dimensional Eulerian framework. The model has 9 state variables including particulate organic carbon, particle-attached bacteria, free-living bacteria, active exoenzyme in the particle, inactive exoenzyme in the particle, hydrolysate in the particle, hydrolysate in the dissolved environment, active exoenzyme in the dissolved environment, and inactive exoenzyme in the dissolved environment.
Please cite the following paper if you use this code:
Mislan KAS, CA Stock, JP Dunne, and JL Sarmiento. 2014. Group behavior among model bacteria influences particulate carbon remineralization depths. Journal of Marine Research. 72:183-218Acknowledgements:
Ben Marwick, University of Washington, and Rahul Biswas, University of Washington, vetted this code release.
Code Release:
KAS was supported by the Washington Research Foundation Fund for Innovation in Data-Intensive Discovery and the Moore/Sloan Data Science Environments Project at the University of Washington.
Scientific Research and Code Development:
The project was supported by the Carbon Mitigation Initiative at Princeton University which is sponsored by BP and the NOAA Cooperative Institute for Climate Science (NA08OAR4320752).https://zenodo.org/record/1614510.5281/zenodo.16145oai:zenodo.org:16145url:https://github.com/kallisons/MicrobeReminModel_v1.0/tree/v1.0url:https://zenodo.org/communities/carboncycleurl:https://zenodo.org/communities/climatechangeurl:https://zenodo.org/communities/ecologicalmodelsurl:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://www.opensource.org/licenses/GPL-3.0Journal of Marine Research 72 183-218 (2014)decompositiondetrituscarbon cycleoceanbacteriaecosystem modelMartin CurveMicrobial Remineralization Model v1.0info:eu-repo/semantics/othersoftwareoai:zenodo.org:8077482020-01-24T19:23:35Zopenaire_datauser-biogeographyuser-climatechangeuser-ecophysiologyuser-oceanbiogeochemistryMislan, K. A. S.Deutsch, Curtis A.Brill, Richard W.Dunne, John P.Sarmiento, Jorge L.2017-06-13Model results and data used to make future projections of the effects of climate change on the physiology of tuna in the global ocean
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Description:
Coupled Model Intercomparison Project Phase 5 (CMIP5) model results were downloaded from here:
https://esgf-node.llnl.gov/search/cmip5/
World Ocean Atlas (WOA) 2009 data were downloaded from here:
https://www.nodc.noaa.gov/OC5/WOA09/netcdf_data.html
The model results and data should only be used to reproduce the analysis described in this publication:
Mislan, K. A. S., C. A. Deutsch, R. W. Brill, J. P. Dunne, and J. L. Sarmiento. (2017) Projections of climate driven changes in tuna vertical habitat based on species-specific differences in blood oxygen affinity. Global Change Biology.
The Zenodo archive of the code is here:
https://doi.org/10.5281/zenodo.808742
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Instructions:
Download the tar.gz file, unzip, and put the folders in the data folder of the CMIP5_p50_tuna code.https://zenodo.org/record/80774810.5281/zenodo.807748oai:zenodo.org:807748doi:10.5281/zenodo.807747url:https://zenodo.org/communities/biogeographyurl:https://zenodo.org/communities/climatechangeurl:https://zenodo.org/communities/ecophysiologyurl:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/legalcodetunaclimate changeCMIP5oxygentemperaturephysiologybloodCMIP5 P50 Analysis v1.0 for Tuna Species: Source Datainfo:eu-repo/semantics/otherdatasetoai:zenodo.org:25585112020-02-17T22:57:56Zopenaire_datauser-oceanbiogeochemistryEmmanuel DevredMARTI GALI TAPIAS2019-02-06README file for the global DMS and DMSPt fields derived with DMS-SAT
Martí Galí Tàpias, 2019-02-06
Questions and requests can be addressed to:
marti.gali.tapias@gmail.com
Content and data sources
========================
This dataset contains gobal fields of dimethylsulfide (DMS) and dimethylsulfoniopropionate in the sea surface layer, estimated with different remote sensing algorithms.
It is based on ocean colour data publicly available at https://oceancolor.gsfc.nasa.gov/.
Monthly climatological data are provided at 9 km, 1 and 5 degree resolutions on lat-lon grids.
Different satellite products have been used for chlorophyll_a and euphotic layer depth, as indicated in the names of the files:
{Variable}_{PERIOD}_{algorithm}_{spatial resolution}_{chlorophyll product}_{euphotic layer product}.nc
{chlorophyll product}: CHL corresponds to OC4-CI, GSM to Garver-Siegel-Maritorena.
{euphotic layer product}: KD490 corresponds to diffuse attenuation coefficient at 490 nm, ZLEE correspond to Lee et al. algorithm.
Note that CHL is not used in VS07 although it appears in the file name.
Please check Galí et al. 2018 Biogeosciences for details.
This dataset is related to doi.org/10.5281/zenodo.2205131
References
==========
Galí, M., Devred, E., Levasseur, M., Royer, S. J., & Babin, M. (2015). A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns. Remote Sensing of Environment, 171, 171-184. https://doi.org/10.1016/j.rse.2015.10.012
Galí, M., Levasseur, M., Devred, E., Simó, R., & Babin, M. (2018). Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales. Biogeosciences, 15(11), 3497-3519. https://doi.org/10.5194/bg-15-3497-2018
How to cite
===========
This dataset can be freely distributed, but please cite it using its DOI. When relevant, cite also the journal articles mentioned above.
Acknowledgments
================
I acknowledge NASA’s Ocean Biology Processing Group (OBPG) for making data freely available.
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Enjoy!
Martí Galí Tàpias
https://zenodo.org/record/255851110.5281/zenodo.2558511oai:zenodo.org:2558511doi:10.5281/zenodo.2205131doi:10.5281/zenodo.2558510url:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/legalcodeDMS, DMSP, ocean, surface, satellite, remote sensing, sulfur, gasDMS-SAT_GLOBAL_MONTHLY_DMS_DMSPt_CLIM_v1.0.0info:eu-repo/semantics/otherdatasetoai:zenodo.org:1605632020-01-20T15:29:29Zopenaireuser-oceanbiogeochemistryMARÍA VALENTINA FUENTESWILLIAM SENIORIVIS FERMÍNL. TROCCOLI2008-06-27The Manzanares River is one of the most important bodies of water in northeastern Venezuela. However, a
vast influx of industrial and domestic sewage from farm and urban dwellings enters the river without much control by the
pertinent authorities. To ascertain the environmental quality of the river 45 sampling sites were set throughout the area
comprising the high, middle, and low-lying areas of the river basin during three different periods, namely, rainy season (2003),
transitional period (2003), and dry season (2004). The parameters monitored and measured following APHA-AWWA-WPCF
standards were: temperature (18.3 – 30.0°C), pH (6.04 – 8.88), dissolved oxygen (1.7 – 7.0 mg.L-1k), nitrogen (11 – 188
μmol.1-1) and phosphorus compounds (nondetectable – 22 μmol.1-1), silicates (8 – 260 μmol.1-1), and fecal coliforms (1 – 2.4
· 107 MPN/100 mL). All parameters yielded differences in the three watersheds and the three seasons. The low-lying area
resulted most affected. Nitrates and nitrites were the prevailing inorganic matter, in consonance with a high presence of fecal
coliforms, profuse decomposed organic matter, and warmer temperatures. Results attest to the poor environmental quality of
the Manzanares River, the waters of which being unsuited for human exposure.Bol. Inst. Oceanogr. Venezuela, 47 (2): 149-158 (2008)
http://ojs.udo.edu.ve/index.php/boletiniov/article/view/848/675https://zenodo.org/record/16056310.5281/zenodo.160563oai:zenodo.org:160563url:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/legalcodeNourishing elements, water, Manzanares River, pollution, edo. Sucre, VenezuelaESTUDIO FISICOQUÍMICO Y BACTERIOLÓGICO DEL RÍO MANZANARES, ESTADO SUCRE, VENEZUELAinfo:eu-repo/semantics/articlepublication-articleoai:zenodo.org:11377952020-01-25T19:22:23Zsoftwareuser-oceanbiogeochemistryHenry C. Bittig2018-01-08Provides Matlab functions as well as example workspaces for recalculations of Oxygen Optode calibration coefficients from manufacturer calibration sheets using different mathematical models as described in Bittig et al. (2017), http://dx.doi.org/10.3389/fmars.2017.00429.https://zenodo.org/record/113779510.5281/zenodo.1137795oai:zenodo.org:1137795url:https://github.com/HCBScienceProducts/Optode_Calculations/tree/v1.0doi:10.3389/fmars.2017.00429doi:10.5281/zenodo.1137794url:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://www.opensource.org/licenses/GPL-3.0Oxygen Optode Calibration Recalculations after Bittig et al. (2017), v1.0info:eu-repo/semantics/othersoftwareoai:zenodo.org:10090652020-01-25T19:23:03Zsoftwareuser-oceanbiogeochemistryDutay, Jean-ClaudeRoy-Barman, Matthieuvan Hulten, Marco2017-10-11ProThorP [pɹoθɔrp] is a model of thorium, protactinium and particles. More specifically, at the moment, it is a conceptual model that describes, and a numerical model that prognostically simulates, thorium-231, protactinium-230 and lithogenic particles derived from deposited dust. In this instance it is used in combination with [NEMO 3.6](https://www.nemo-ocean.eu/).https://zenodo.org/record/100906510.5281/zenodo.1009065oai:zenodo.org:1009065arxiv:arXiv:1708.04157doi:10.5281/zenodo.1009064url:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/4.0/legalcodeoceanography, thorium-230, protactinium-231, particles, modellingOcean model of Protactinium, Thorium and Particles (ProThorP)info:eu-repo/semantics/othersoftwareoai:zenodo.org:37763772020-06-06T22:03:30Zopenaire_datauser-oceanbiogeochemistryDEMARCQ HervéDEMARCQ Hervé2020-05-11This data set is the result of the calculation of an original “enrichment index” (EI) from chlorophyll-a (chl-a) remote sensing data (MODIS-Aqua sensor) and initially dedicated to highlight localized chl-a enrichments associated to isolated seamounts and islands in the South West Indian Ocean, in order to estimate their contribution in increasing the local primary productivity. Details and results are described in the DSR-II paper entitled “Satellite observations of phytoplankton enrichments around seamounts in the South West Indian Ocean with a special focus on the Walters Shoal” from Demarcq et al. 2020.
1. Initial data used
We used daily L3 data chl-a and sea surface temperature (SST) collected by the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor on board the Aqua platform (downloaded from https://oceancolor.gsfc.nasa.gov/) from January 2003 to December 2018. This has a spatial resolution of 1/24° (ca. 4.5–5 km). The data covers the region (45°S – 10°S / 25°W – 80°W).
2. The calculation method
The calculations were done at the pixel level. The EI is the difference (expressed in %) between the value of each ‘candidate pixel’ and its medium range surrounding, defined as the average value of all chl-a values around the candidate pixel between a fix range of distance between 30 and 90 km, the R1 and R2 terms of the equation enclosed.
3. Data sets
The data set contains two files:
- the monthly climatology (12 frames) of the EI from January to December (2003 to 2018 average), in an internally compressed netCDF-4 format (NC-compliant or almost)
- the yearly average of the EI (period 01/2003 - 12/2018)
Two images are joined with this data set:
- a "technical view" of the yearly average of the index for the full region sub-region (45°S – 10°S / 25°W – 80°W)
(file: indsw4_modis_p100_4km_16y_20030101_20181231.R2018.0.enrichment-index.dist-30-90km.png).
- a slightly improved view of the yearly average of the index for the sub-region (40°S – 10°S / 30°W – 70°W).
(file: Figure-enrichment-index.pdf)
An improved version of this index will be available in a near future.e-mail contact: herve.demarcq@ird.frhttps://zenodo.org/record/377637710.5281/zenodo.3776377oai:zenodo.org:3776377engdoi:10.5281/zenodo.3776376url:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/legalcodeDeep-sea Research IIsurface enrichmentSouth West Indian Oceanchlorophyll-aremote sensingMODIS-AquaEnrichment index related to seamounts and islands in the South West Indian Ocean from chlorophyll-a satellite remote sensing datainfo:eu-repo/semantics/otherdatasetoai:zenodo.org:38627612020-06-06T22:03:18Zopenaire_datauser-oceanbiogeochemistrySosa, Oscar A.Gonnelli, MargheritaSantinelli, ChiaraKarl, David M.Granzow, Benjamin N.Repeta, Daniel J.2020-05-28info:eu-repo/date/embargoEnd/2020-10-01These datasets contain oceanic enzyme activity measurements of Carbon-Phosphorus (C-P) lyase, a catalytic enzyme pathway, which cleaves C-P bonds, allowing microbial utilization of phosphonates as a phosphorous source. These activity measurements were made in the North Pacific Subtropical Gyre. Samples were collected in the top 1000 m of the water column on KM1717, KM1821, and KM2001 cruises and in the top 225 m of the water column on FK180310-2.https://zenodo.org/record/386276110.5281/zenodo.3862761oai:zenodo.org:3862761engdoi:10.5281/zenodo.3862760url:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/embargoedAccesshttp://creativecommons.org/licenses/by/4.0/legalcodeC-P lyaseEnzyme ActivitymarinephosphonateNorth Pacific Subtropical GyreCarbon-Phosphorous Lyase Enzyme Activity Profiles in the North Pacific Subtropical Gyreinfo:eu-repo/semantics/otherdatasetoai:zenodo.org:1605262020-06-06T12:49:45Zopenaireuser-oceanbiogeochemistryuser-biogeochemAristide MárquezOrlys GarciaWilliam SeniorGregorio MartinezÁngel González2012-03-22La concentración total y las formas qumicas de los metales pesados se determinaron en sedimentos superficiales del Orinoco Medio, Venezuela, utilizando una solución 25% (v/v) de ácido acético/solución HNO3: HCl: HClO4 (3:2:1), espectrofotometra de absorción atómica con llama y técnica de vapor en frio. El rango de los valores totales variaron entre 8871 a 116.759 μgFeg-1, 102,45 a 469,44 μgMn g-1; 0,93 a 17,64 μgCu g-1; 4,46 a 17,48 μgNi g-1; 2,46 a 9,61 μgCo g-1; 42,56 a 181,45 μgZn g-1; 1,29 a 8,76 μgCr g-1; 0,03 a 0,74 μgCd g-1 and 0,001 a 7,88 μgPb g-1. Los metales están fuertemente asociados a la fracción que contiene los oxihidróxidos de hierro más resistente, sulfuros metálicos, minerales residuales refractarios y materia orgánica. Los rangos fueron: 7,50-99,29% Fe; 7,75-66,34% Mn; 22,55-98,89% Zn; 22,85-91,36% Ni; 4,20-85,03% Cu; 16,76-85,48% Co; 12,56-95,49 Cr; 7,50-99,29% Pb; 2,03-85,48% Cd. Los valores de metales adsorbidos en la superficie de las partculas, asociados con los carbonatos y los oxihidróxidos de manganeso reactivos variaron entre: 0,04-1,97% Fe; 4,15-71,59% Mn; 0,86-3,83% Zn; 0-12,10% Ni; 1,05-14,97% Cu; 6,40-33,06% Co; 1,03-5,08% Cr; 0-1,78% Pb; 0-22,97% Cd. Las concentraciones totales de Cu, Ni, Zn y Pb, son superiores a los reportados en la literatura para sedimentos no contaminados en algunas estaciones como los puertos de las ciudades de Cabruta y Caicara del Orinoco.Ciencia 20(1) 60-73, 2012, Maracaibo, Venezuela
www.produccioncientifica.luz.edu.ve/index.php/ciencia/article/view/10038https://zenodo.org/record/16052610.5281/zenodo.160526oai:zenodo.org:160526url:https://zenodo.org/communities/biogeochemurl:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/legalcodeMetals, sediment, distribution, Orinoco river, edo. Bolvar, VenezuelaDistribucion de metales pesados en sedimentos superficiales del Orinoco Medio, Venezuelainfo:eu-repo/semantics/articlepublication-articleoai:zenodo.org:38788352020-06-06T22:05:27Zuser-biogeochemuser-oceanbiogeochemistryJeemijn ScheenThomas F. Stocker2020-06-05
This dataset contains the output of model simulations used in the paper:
Scheen, Jeemijn and Stocker, Thomas F., "Effect of changing ocean circulation on deep ocean temperature in the last millennium", submitted (2020)
All figures can be reproduced when combining this dataset with the analysis code published on github.
Download either the small (unzipped 4 Gb) or large (unzipped 22 Gb) version of the dataset. Warning: this is not only about storage, but this amount needs to be loaded into memory when running the notebook. You only need the small version to run the github notebook and reproduce the figures, but you are free to explore additional variables in the large version.
https://zenodo.org/record/387883510.5281/zenodo.3878835oai:zenodo.org:3878835enginfo:eu-repo/grantAgreement/SNSF/Project+funding/200020_172745/doi:10.5281/zenodo.3878834url:https://zenodo.org/communities/biogeochemurl:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/legalcodeclimate modellingdeep ocean temperatureocean circulationlittle ice ageEffect of changing ocean circulation on deep ocean temperature in the last millennium: simulation output datainfo:eu-repo/semantics/otherdatasetoai:zenodo.org:25565302020-05-18T14:10:13Zopenaireuser-oceanbiogeochemistryVervatis, V.D.Pierre De Mey-FrémauxNadia AyoubSarantis SofianosCharles-Emmanuel TestutMarios KailasJohn KaragiorgosMalek Ghantous2019-02-04We complement the NEMO stochastic modules to calculate explicitly variable 2D spatial scales. The FORTRAN subroutine integrated within the “stopar.F90” module is compatible with the NEMO MPI environment.
Acknowledgments.
This work was carried out as part of the Copernicus Marine Environment Monitoring Service (CMEMS) “Stochastic Coastal/Regional Uncertainty Modelling” - SCRUM and SCRUM2 projects. CMEMS is implemented by Mercator Ocean International in the framework of a delegation agreement with the European Union.
Part of this research was also made possible through the IKY Scholarships Programme and co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the action entitled “Reinforcement of Postdoctoral Researchers”, in the framework of the Operational Programme “Human Resources Development Programme, Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) 2014-2020.
We acknowledge the use of ECMWF's computing and archive facilities in this research.
This work was also supported by computational time granted from the Greek Research & Technology Network (GRNET) in the National HPC facility – ARIS – under project ID PA002007.
cite:
Vervatis, D. V., P. De Mey-Frémaux, et al., Assessment of a physical-biogeochemical stochastic model in a high-resolution Bay of Biscay configuration. Part 1: generation and first analysis, Ocean Modelling, submitted.
Vervatis, D. V., P. De Mey-Frémaux, et al., Assessment of a physical-biogeochemical stochastic model in a high-resolution Bay of Biscay configuration. Part 2: empirical consistency, Ocean Modelling, submitted.https://zenodo.org/record/255653010.5281/zenodo.2556530oai:zenodo.org:2556530doi:10.5281/zenodo.2556529url:https://zenodo.org/communities/oceanbiogeochemistryinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/legalcodeNEMOSCRUMSCRUM2CMEMSService EvolutionPhysical-biogeochemical regional ocean model uncertainties stemming from stochastic parameterizationsinfo:eu-repo/semantics/articlepublication-article